1. Field of the Invention
The present invention relates to reducing the presence of interference with a desired signal. More specifically, the present invention relates to the use of micro-electromechanical systems (“MEMS”) filters to reduce the presence of interfering signals in the same frequency range of desired signals.
2. Discussion of Background Information
Antennas often receive radio frequency transmissions that include both a desired signal and substantial undesirable interference in adjacent frequencies. Since signal strength and amplitude is directly related to the distance of the signal source to the antenna, the desired signal (which typically emanates from a distant signal source) may be much weaker than interference emanating locally relative to the antenna. FIG. 1 shows an example of how a signal 100 includes desired signal 102 intermixed with much stronger interference signals 104. The challenge is to recover a desired weak signal that is proximate to stronger interference signals. The receiver front end must be able to accommodate the strong signal without saturation and have enough dynamic range to recover the weak desired signal.
It is therefore desirable to initially pass the received signal through a front end filter before processing the received signal to remove as much interference as possible. However, such efforts have not proven effective for high frequency signals, as the bandwidth of the signal (typically on the order of 10,000 Hz) is much larger than the bandwidth of typical band pass filters (typically on the order of 500 Hz). FIG. 2 shows an example of the signals from FIG. 1 being filtered according to this method. While the filter is effective to remove out of band interferers, it has no effect on the interferers within the bandwidth of the filter. Such filters may also prove ineffective if there are any desirable signals that fall outside of the range of the filter.
Recently efforts have been made to use MEMS resonators as band pass filters limited to the signal(s) of interest. FIG. 3 shows an example of such a MEMS resonator, and FIG. 4 shown an example of two MEMS resonators connected to form a MEMS filter. In theory, the bandwidth of such a MEMS filter can be made consistent with the bandwidth of the desired signal, so that the MEMS filter could filter out all interferences other than the desired signal. Referring now to FIG. 5, a series of such filters could be theoretically set for different adjacent bandwidths to cover the entire frequency band of interest; depending on the frequency of the signals of interest, individual filters could be turned ON to only allow those signals of interest 102 to pass while all other signals are filtered out.
The theory has not proven effective in practice because of a lack of stability in MEMS resonators. The methodology shown in FIG. 5 requires that each MEMS filter maintain its position on the frequency spectrum. However, the specific resonance frequency of a MEMS filter is also highly dependent on the shape of the components, which is subject to variances in manufacturing accuracy and tolerance during initial fabrication. Even if perfectly manufactured, the resonance frequency of each filter (i.e., the range of frequency between the lower cutoff frequency and the upper cutoff frequency of the filter) is expected to shift due to physical changes in the geometry of the resonator due to changes in external temperature and aging. As the resonance frequencies of individual filters diverge, the various bandwidths will either begin to overlap or expose gaps therebetween. This will in turn allow for additional interferences to pass through, as well as possibly filtering out the desirable signal.
Another drawback of the use of MEMS filters in this manner is that the filter processes the desired signal. The filter must therefore be highly linear as not to distort the signal. This places additional requirements on the design and precision of the individual MEMS components.
Still another drawback of the use of MEMS filters in this manner is that because the signal of interest tends to be much smaller than the noise, it may be difficult to find the signal of interest if its specific frequency is not known.